In this thesis, we solve a mean-variance portfolio optimization problem with portfolio constraints under a regime-switching model. Specifically, we seek a portfolio process which minimizes the variance of the terminal wealth, subject to a terminal wealth constraint and convex portfolio constraints. The regime-switching is modeled using a finite state space, continuous-time Markov chain and the market parameters are allowed to be random processes. The solution to this problem is of interest to investors in financial markets, such as pension funds, insurance companies and individuals. We establish the existence and characterization of the solution to the given problem using a convex duality method. We encode the constraints on the given problem as static penalty functions in order to derive the primal problem. Next, we synthesize the dual problem from the primal problem using convex conjugate functions. We show that the solution to the dual problem exists. From the construction of the dual problem, we find a set of necessary and sufficient conditions for the primal and dual problems to each have a solution. Using these conditions, we can show the existence of the solution to the given problem and characterize it in terms of the market parameters and the solution to the dual problem. The results of the thesis lay the foundation to find an actual solution to the given problem, by looking at specific examples. If we can find the solution to the dual problem for a specific example, then, using the characterization of the solution to the given problem, we may be able to find the actual solution to the specific example. In order to use the convex duality method, we have to prove a martingale representation theorem for processes which are locally square-integrable martingales with respect to the filtration generated by a Brownian motion and a finite state space, continuous-time Markov chain. This result may be of interest in problems involving regime-switching models which require a martingale representation theorem.

In this thesis, we solve a mean-variance portfolio optimization problem with portfolio constraints under a regime-switching model. Specifically, we seek a portfolio process which minimizes the variance of the terminal wealth, subject to a terminal wealth constraint and convex portfolio constraints. The regime-switching is modeled using a finite state space, continuous-time Markov chain and the market parameters are allowed to be random processes. The solution to this problem is of interest to investors in financial markets, such as pension funds, insurance companies and individuals. We establish the existence and characterization of the solution to the given problem using a convex duality method. We encode the constraints on the given problem as static penalty functions in order to derive the primal problem. Next, we synthesize the dual problem from the primal problem using convex conjugate functions. We show that the solution to the dual problem exists. From the construction of the dual problem, we find a set of necessary and sufficient conditions for the primal and dual problems to each have a solution. Using these conditions, we can show the existence of the solution to the given problem and characterize it in terms of the market parameters and the solution to the dual problem. The results of the thesis lay the foundation to find an actual solution to the given problem, by looking at specific examples. If we can find the solution to the dual problem for a specific example, then, using the characterization of the solution to the given problem, we may be able to find the actual solution to the specific example. In order to use the convex duality method, we have to prove a martingale representation theorem for processes which are locally square-integrable martingales with respect to the filtration generated by a Brownian motion and a finite state space, continuous-time Markov chain. This result may be of interest in problems involving regime-switching models which require a martingale representation theorem.

Hidden Markov models have become a popular tool for modeling long-term investment guarantees. Many different variations of hidden Markov models have been proposed over the past decades for modeling indexes such as the S&P 500, and they capture the tail risk inherent in the market to varying degrees. However, goodness-of-fit testing, such as residual-based testing, for hidden Markov models is a relatively undeveloped area of research. This work focuses on hidden Markov model assessment, and develops a stochastic approach to deriving a residual set that is ideal for standard residual tests. This result allows hidden-state models to be tested for goodness-of-fit with the well developed testing strategies for single-state models. This work also focuses on parameter uncertainty for the popular long-term equity hidden Markov models. There is a special focus on underlying states that represent lower returns and higher volatility in the market, as these states can have the largest impact on investment guarantee valuation. A Bayesian approach for the hidden Markov models is applied to address the issue of parameter uncertainty and the impact it can have on investment guarantee models. Also in this thesis, the areas of portfolio optimization and portfolio replication under a hidden Markov model setting are further developed. Different strategies for optimization and portfolio hedging under hidden Markov models are presented and compared using real world data. The impact of parameter uncertainty, particularly with model parameters that are connected with higher market volatility, is once again a focus, and the effects of not taking parameter uncertainty into account when optimizing or hedging in a hidden Markov are demonstrated.

In order to catch up the international trend, ¡§Expensing employee bonus¡¨ has been implemented in Taiwan since year 2008. Hence, all the cost concerning employees¡¦ bonuses have been recorded as expense in the income statement and recognized by fair market value. Since the company decides total amount of employees¡¦ bonuses after authorized by the board and annual general meeting, it can distribute the proportion of cash and stock bonuses. As the result of calculating the stock bonus by stock¡¦s fair value, employees gain much less stocks than before, which lessen the encouragement effect. Therefore, enterprises begin to increase the standard salary of employee or proportion of cash bonus. This study collects the data from the fourth quarter of year 1989 to the third quarter of 2009, and chooses the Taiwan Weighted Stock Index and the stock prices of listed electronic firms in Taiwan. Using the Markov Regime Switching Model as the research method, and add the macroeconomic and financial variables to separate the stock price into two regimes- recession and expansion regime. This research is in the employee¡¦s shoes, and to study what stock incentives strategies the company should adopt under the required expensing of employee stock bonus. The empirical results are summarized as follows: 1.Under the expansion regime, if the company¡¦s stock price was affected by both macroeconomic and financial variables, it will more likely rise further, which leads to the large gap between two regimes. For example: Cyberlink, Acer and Mediatec, which stock price gaps are over ten dollars. 2.According to the two arguments of this study: the company with long duration of expansion regime and is influenced by macroeconomic and financial variables should adopt the strategies based on stock bonus. Therefore, according to the empirical results, the study suggests that Acer is the suitable company to do the strategies.

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In this paper, we use a Fama-French model and Markov regime-switching model to capture time series behavior of many financial variable. Alternatively, classification by cluster analysis help to learn the different characteristics of the sample between stock returns and risk factors. This empirical result shows that the excess return in the low volatility state tends to be greater than that in the high volatility state. The stock returns in each regime have a higher probability of remaining in their original state, especilly in low volatility state. This article also found the influence of risk factors affecting the stock returns is not symmetrical. In the state of low volatility, market factors and momentum effect have a significant influence in stock returns, and in the high volatility state, except the size effect, market and behavior factors have a significant influence in stock returns. Markov-switching models have proved to be useful for modeling a range of economic time series in the stock market. The regime-switching model has a superior performance in capturing the risk sensitivities of the stock return beyond the findings based on the Fama-French models. At last, we find the cluster analysis is feasible for the multi-factor model. The returns of mature companies have a primarily impact of market risk premium, while the major factor affecting returns with characteristics of growth companies is a investor sentiment. In addition, it is found that small companies¡¦ returns are vulnerable to investors sentiment. In this case, investors will invest based on stock's past performance, so the momentum effect significantly affect the stock returns.

In order to get maximum profit, investors start to high attention on risk management after financial crisis in 2008. Therefore, risk management and predict become more and more complex. This paper mainly focuses on two risks, including non-systematic risk and credit risk. After financial crisis, countries pay more attention on credit risk, and now because of Europe debt crisis, investors and governments are also concerned with the messages about credit rating which are published by Credit Rating Agency. Besides credit risk, the firm¡¦s specific risk (i.e. non-systematic risk) is also more important than before. Recent empirical studies find that the stock is not on affected by systematic risk, but also affected by non-systematic risk. According to Kuo and Lu (2005), this thesis uses two models: Moody¡¦s KMV credit model and Markov regime switching model to estimate credit risk and non-systematic risk. The period is from January 2002 to November 2010. Testing samples are data from constituent stocks of the Taiwan 50. The purpose of this paper is researching the relationship between credit risk and non-systematic risk. The empirical results show that there is the positive relationship between non-systematic risk and credit risk. And among different industries, non-systematic risk or credit risk also shows the significant differences. For plastic industry and communications network industry, there is lower credit risk. However, for electronics industry and financial industry, there is higher credit risk. The study also found that even in the same industry, each company will face different risk level.

Since reformed of banking structure in China, banks have been gradually developed their operation system. Moreover, the restructure in commercial bank after joined WTO had established China¡¦s banks performance and international reputation. Since 2007, many large commercial banks have strength its risk management based on the commitments made by China Banking Regulatory Commission (CBRC) to follow the New Basel Capital Accord. When the global banking industry is devastated by global financial crisis (GFC) during 2008, China¡¦s banks are less affected by GFC. In addition, the capital scale and revenues performance were thrived during GFC. Therefore, it shows that banks in China had improved the resilience ability during financial crisis. However, being originated in China¡¦s loose monetary policy and economic stimulus package after GFC, investors worried that domestic banks might bear high risks. Notably, the risk is specific risk from each bank instead of system risk. This study employs Markov regime-switching model to examine 14 China banks¡¦ stock prices. The empirical evidence supports our hypothesis that behavior of China banks¡¦ stock prices has confronted structural change after GFC. Furthermore, this research presents that unsystematic risks from each bank were significantly decreased after GFC. It indicates that investors are too pessimistic on the banks in China might suffer high risk after government interventions.

This study tires to find out how a firm¡¦s advertising and customer satisfaction influence firms¡¦ abnormal return and we uses the abnormal return (i.e. Jensne¡¦s £\) as the proxy of firm¡¦s shareholder value. We expect firms¡¦ advertising and customer satisfaction will have a positive impact on abnormal return while having a negative impact on firms¡¦ risk. In addition, we also consider under different market state whether advertising and customer satisfaction have an asymmetric effect. Compare with Carhart (1997) four factor model, this paper also takes the factor of VIX into account, and we use Markov regime switching model to recognize bull market and bear market because it can help us get a more accurate estimation. We choose the Generalized method of moments (GMM) to estimate the impact of advertising and customer satisfaction on shareholder value and discuss that whether advertising and customer satisfaction are able to lift up shareholder value or not. The outcome shows that advertising doesn¡¦t have significantly positive impact on firms¡¦ abnormal return under bull market and bear market. However, customer satisfaction has a significantly positive relationship with firms¡¦ abnormal return under bull market and bear market. And we find that if firms maintain the level of customer satisfaction under bear market, it will be more efficiently to lift up firms¡¦ abnormal return rather than spending more money on advertising.

This dissertation investigates the pricing behaviors of two major energy commodities, U.S. natural gas and crude oil, using times series models. It examines the relationships between U.S. natural gas price variations and changes in market fundamentals within a two-state Markov-switching framework. It is found that the regime-switching model does a better forecasting job in general than the linear fundamental model without regime-switching framework, especially in the case of 1-step-ahead forecast. Studies are conducted of the dynamics between crude oil price and U.S. dollar exchange rates. Empirical tests are applied to both full sample (1986—2010) and subsample (2002—2010) data. It is found that causality runs in both directions between the oil and the dollar. Meanwhile, a theoretical 5-country partial dynamic portfolio model is constructed to explain the dynamics between oil and dollar with special attention to the roles of China and Russia. It is shown that emergence of China‘s economy enhances the linkage between oil and dollar due to China's foreign exchange policy. Further research is dedicated to the role of speculation in crude oil and natural gas markets. First a literature review on theory of speculation is conducted. Empirical studies on speculation in commodity markets are surveyed, with special focus on energy commodity market. To test the theory that speculation may affect commodity prices by exaggerating the signals sent by market fundamentals, this essay utilizes the forecast errors from the first essay to investigate the forecasting ability of speculators' net long positions in the market. Limited evidence is provided to support the bubble theory in U.S. natural gas market. In conclusion, this dissertation explores both fundamentals and speculators' roles in the U.S. natural gas and global crude oil markets. It is found that market fundamentals are the major driving forces for the two energy commodities price booms seen during the past several years.